Galton Biometrics has licenced a unique biometric technology from the USA .  Our product provides several unique developments in the area of biometric fingerprint technology that enable fast individual authentication, precise identification searches, as well as cross-platform data interoperability. Their software solution is a highly accurate method of generating a short, universal, numeric identifier based on a person’s fingerprint. This encrypted Biometric Identifier Number (BIN) can be used with personal credentials such as passports, Visa’s, and ID cards such as Bank Cards, Drivers Licenses, Health Cards, etc., for one-to-one guaranteed authentication. Galton is the distributor of this innovate technology in Europe and owns the IP rights in the UAE.

Our product offerings address national security concerns regarding privacy and data integrity. From consular services to terrorist watch lists, our innovative technology allows for seamless, integrated identification searches across disparate databases at all levels of government and law enforcement

The company is also continuing to develop its innovative biometric technology to extract a unique short code template based on a persons fingerprint pattern. This short number based on the advanced and non-traditional patented approach to the analysis and encoding of fingerprints,

This development is part of Galton’s very rapid identification search system to be used against immigration databases and terrorist/law enforcement watch lists, potentially requiring only a fraction of the processing speed currently needed by more conventional fingerprint identification methods

Galton through its master license develops revolutionary fingerprint biometric solutions enabling fast individual authentication, precise identification searches, as well as cross-platform data interoperability. Our innovative, scalable technology extracts a fixed-length, near-constant number based on one’s fingerprint pattern. This short number becomes a precise and universal biometric identifier capable of being embedded in travel documents, such as visas and passports, credit cards, driver’s licenses and a host of other personal credentials.

Our product offerings address national security concerns regarding privacy and data integrity. From consular services to terrorist watch lists, our innovative technology allows for seamless, integrated identification searches across disparate databases at all levels of government and law enforcement

The Industry

Global 2002 industry revenues of are expected to reach .04b by 2007, driven by large-scale public sector biometric deployments, the emergence of transactional revenue models, and the adoption of standardized biometric infrastructures and data formats.

Fingerprint-based technologies, including both finger-scan and AFIS, are projected to account for of 2002 industry revenues, far and away the largest technology segment. This growth is attributable to the wide range of applications in which fingerprint- based solutions operate effectively.

Civil ID and PC / Network Access will be the leading biometric applications over the next five years, expected to account for nearly in combined annual revenues in 2007. Physical Access / Time and Attendance will reach in annual revenues by 2004, with Surveillance and Screening applications projected to reach in annual revenue in 2004.

The Government sector will be the leading biometric vertical market through 2007 with $1.2b in annual revenues. The Financial and the Travel/Transportation sectors follow with and , respectively, in 2007. The various scenarios in which government agencies must identify and authenticate both citizens and employees, particularly subsequent to 9/11, is a critical growth factor.

The History of the FingerPrint

Among all the biometric techniques, fingerprint-based identification is the oldest method which has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints. A fingerprint is made of a series of ridges and furrows on the surface of the finger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.

Fingerprint matching techniques can be placed into two categories: minutae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. Also this method does not take into account the global pattern of ridges and furrows. The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach. However, it has some of its own shortcomings. Correlation-based techniques require the precise location of a registration point and are affected by image translation and rotation.

Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns. Local ridge structures can not be completely characterized by minutiae. We are trying an alternate representation of fingerprints which will capture more local information and yield a fixed length code for the fingerprint. The matching will then hopefully become a relatively simple task of calculating the Euclidean distance will between the two codes.

We are developing algorithms which are more robust to noise in fingerprint images and deliver increased accuracy in real-time. A commercial fingerprint-based authentication system requires a very low False Reject Rate (FAR) for a given False Accept Rate (FAR). This is very difficult to achieve with any one technique. We are investigating methods to pool evidence from various matching techniques to increase the overall accuracy of the system. In a real application, the sensor, the acquisition system and the variation in performance of the system over time is very critical. We are also field testing our system on a limited number of users to evaluate the system performance over a period of time.

Fingerprint Classification:

Large volumes of fingerprints are collected and stored everyday in a wide range of applications including forensics, access control, and driver license registration. An automatic recognition of people based on fingerprints requires that the input fingerprint be matched with a large number of fingerprints in a database (FBI database contains approximately 70 million fingerprints!). To reduce the search time and computational complexity, it is desirable to classify these fingerprints in an accurate and consistent manner so that the input fingerprint is required to be matched only with a subset of the fingerprints in the database.